KR102063613B1 - Exercise prescription apparatus and method - Google Patents

Abstract

A computing device for generating workout prescription data is provided. The computing device may include a communication unit for receiving exercise result data for a user from a designated device, an input unit for obtaining feedback information on the received exercise result data, an exercise type with reference to disease information and state information from a database about the user, A determination unit determining first exercise prescription data including an exercise intensity, an exercise count, and an exercise time; and a correction unit configured to correct the first exercise prescription data according to the feedback information to generate second exercise prescription data of the user. It may include.

Description

EXERCISE PRESCRIPTION APPARATUS AND METHOD}

The following description relates to exercise prescription devices and methods.

Today, with the development of wireless communication technology and increasing interest in health, various health care devices are on the market. Among them, walking is a basic exercise that can be performed by anyone of all ages, regardless of time and place. It is one of the most rehabilitation exercises for patients who are uncomfortable with surgery or chemotherapy to regain strength.

There is an increasing need for a device that prescribes an appropriate amount of exercise suitable for each user's health and monitors the amount of exercise of the user in daily life to help maintain health.

Republic of Korea Patent No. 10-1721768 (announced March 30, 2017) is an invention related to a device for providing a target calorie corresponding to the user's health state, and monitoring the walking distance and walking speed of the user. More specifically, the registered patent includes a configuration of generating the target calories by adjusting the standard calories based on the medical history data of the user, and providing the appropriate walking distance and walking speed to the user terminal.

According to one side, a computing device for generating workout prescription data is provided. The computing device may include a communication unit for receiving exercise result data for a user from a designated device, an input unit for obtaining feedback information on the received exercise result data, an exercise type with reference to disease information and state information from a database about the user, A determination unit determining first exercise prescription data including an exercise intensity, an exercise count, and an exercise time; and a correction unit configured to correct the first exercise prescription data according to the feedback information to generate second exercise prescription data of the user. It may include.

According to an embodiment, the determination unit selects survival probability data matching the disease information from the database, and determines the first exercise prescription data according to a physical ability indicating survival probability above a threshold in the selected survival probability data. Can be. The determining unit may select survival probability data corresponding to the cancer type by referring to the cancer type corresponding to the user from the database, and the survival probability graph corresponds to a metabolic equivalent task (MET) range. It may include a survival probability over time corresponding to.

According to another embodiment, the input unit may call a check list for identifying symptoms for each of lymphedema, peripheral neuropathy, immune suppression, fatigue symptoms, and musculoskeletal disorders based on the received exercise result data. The computing device may further include an output unit for comparing the exercise result data with the second exercise prescription data and outputting a notification message to the user according to the comparison result. The output unit outputs a graphic object corresponding to the check list, and the input unit obtains the feedback information according to a user interaction with respect to the graphic object.

According to another embodiment, the communication unit updates the exercise result data of the user according to a predetermined time period, and the output unit is based on the exercise amount difference value of the second exercise prescription data and the updated exercise result data A notification message can be output to the user.

According to another embodiment, the communication unit receives the exercise result data including the seating time of the user, the output unit is the difference between the seating time limit and the seating time of the user included in the second exercise prescription data The notification message may be output to the user based on the value.

According to another embodiment, the communication unit may receive the cumulative exercise result data of the user summed up for a predetermined period, and the correction unit may correct the second exercise prescription data using the cumulative exercise result data.

According to another aspect, an exercise prescription method is provided. The exercise prescription method may include obtaining feedback information on the received exercise result data, querying disease information and state information from a database regarding the user, exercise type and exercise intensity according to the inquired disease information and state information. Determining first exercise prescription data including the number of exercise and exercise time, and correcting the first exercise prescription data according to the feedback information to generate second exercise prescription data of the user. have.

According to an embodiment, the querying of the disease information and the status information may include querying risk group information on cancer type information and symptoms corresponding to the user from the database. The determining of the first exercise prescription data may include selecting survival probability data according to cancer type information corresponding to the user, and selecting the first exercise prescription data according to a physical ability indicating a survival probability above a threshold in the selected survival probability data. Determining prescription data.

According to another embodiment, the obtaining of the feedback information may include checking a check list for identifying symptoms for each of lymphedema, peripheral neuropathy, immunodeficiency, fatigue symptoms, and musculoskeletal disorders based on the received exercise result data. And obtaining the feedback information according to the calling and the user interaction associated with the check list.

According to another embodiment, the exercise prescription method may further include comparing the exercise result data with the second exercise prescription data, and outputting a notification message to the user according to the comparison result.

According to another embodiment, the exercise prescription method may further include updating risk group information about symptoms in the database by using the obtained feedback information.

According to yet another aspect, there is provided a computer-readable recording medium containing a program for executing an exercise prescription method. The program includes a command set for obtaining feedback information on the received exercise result data, a command set for querying disease information and state information from a database about the user, an exercise type and an exercise intensity according to the inquired disease information and state information. And a set of instructions for determining first workout prescription data including a number of workouts and a workout time, and a set of instructions for correcting the first workout prescription data according to the feedback information to generate second workout prescription data of the user. can do.

1 is an exemplary diagram illustrating an exercise prescription process between a smart insole, an exercise prescription device, and a user, according to an exemplary embodiment.
2A and 2B are exemplary views illustrating a process of obtaining feedback information from a user according to an embodiment.
3 is a flowchart illustrating a process of determining, by an exercise prescription device, first exercise prescription data according to an embodiment.
4 is an exemplary diagram illustrating survival probability data according to an embodiment.
5 is a flowchart illustrating a process of generating, by the exercise prescription device of FIG. 3, second exercise prescription data using feedback information.
6 is an exemplary view illustrating a process of outputting a notification message to a user by the exercise prescription device according to another embodiment.
Figure 7 is a block diagram of an exercise prescription device according to another embodiment.

Specific structural or functional descriptions of the embodiments are disclosed for purposes of illustration only, and may be practiced in various forms. Accordingly, the embodiments are not limited to the specific disclosure, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical idea.

Terms such as first or second may be used to describe various components, but such terms should be interpreted only for the purpose of distinguishing one component from another. For example, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.

When a component is referred to as being "connected" to another component, it should be understood that there may be a direct connection or connection to that other component, but there may be other components in between.

Singular expressions include plural expressions unless the context clearly indicates otherwise. As used herein, the terms "comprise" or "have" are intended to designate that the stated feature, number, step, operation, component, part, or combination thereof is present, but one or more other features or numbers, It should be understood that it does not exclude in advance the possibility of the presence or addition of steps, actions, components, parts or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in the commonly used dictionaries should be construed as having meanings consistent with the meanings in the context of the related art, and are not construed in ideal or excessively formal meanings unless expressly defined herein. Do not.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. In the description with reference to the accompanying drawings, the same components will be given the same reference numerals regardless of the reference numerals, and redundant description thereof will be omitted.

Acquisition of exercise result data

1 is an exemplary diagram illustrating an exercise prescription process between a smart insole, an exercise prescription device, and a user, according to an exemplary embodiment. Referring to FIG. 1, a data flow between the smart insole 110, the exercise prescription device 120, and the user 130 is shown. The smart insole 110 may include an inertial sensor measuring acceleration data, velocity data, direction data, and distance data about the user. The smart insole 110 may transmit the exercise result data measured using the inertial sensor to the exercise prescription device 120. For example, the exercise result data may include at least one of a user's sitting time, a user's walking time, a walking distance, an average walking speed, and an exercise amount.

In one embodiment, the smart insole 110 and the exercise prescription device 120 may be connected through a communication interface. Communication interfaces include wireless LAN (WLAN), Wireless Fidelity (WiFi) Direct, Digital Living Network Alliance (DLNA), Wireless broadband (Wibro), World Interoperability for Microwave Access (Wimax), and High Speed Downlink Packet Access (HSDPA). It may include an Internet interface and a short range communication interface such as Bluetooth ™, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, and Near Field Communication (NFC). . In addition, the communication interface may represent all interfaces (eg, wired interfaces) capable of communicating with the outside.

In this embodiment, the smart insole 110 is implemented in the form of an insole in the shoe to help the user to understand, but the exercise prescription device 120 receives the exercise result data from various types of wearable devices such as smart shoes, etc. Embodiments may also be implemented.

The exercise prescription device 120 may output the exercise prescription data generated using the exercise result data to the user 130. More specifically, the exercise prescription device 120 may generate exercise prescription data using exercise result data and feedback information about the exercise. In addition, the exercise prescription device 120 may transmit the exercise prescription data from the user 130 by outputting a graphic object related to the generated exercise prescription data to the user.

Hereinafter, a process of obtaining, by the exercise prescription device 120, feedback information and correcting exercise prescription data using the obtained feedback information will be described in detail.

Acquisition of Feedback Information

2A and 2B are exemplary views illustrating a process of obtaining feedback information from a user according to an embodiment. Referring to FIG. 2A, an example implementation of a graphical object output by an exercise prescription device is shown. The graphical object may include a plurality of check boxes 210 for a risk check list. The exercise prescription device may obtain feedback information according to a user interaction with respect to the plurality of checkboxes 210.

In an embodiment, the user may transmit the feedback information to the exercise prescription device in a manner of touching a portion of the plurality of checkboxes 210 through his user terminal. In another embodiment, the user may transmit the feedback information to the exercise prescription device by clicking a part of the plurality of checkboxes 210 through input means (eg, a mouse and a keyboard) included in the computing device. For example, each of the plurality of checkboxes 210 may indicate whether the user is in an early stage of cancer, whether high-intensity physical activity was performed before the diagnosis of cancer, presence of accompanying diseases, presence of peripheral neuritis, presence of arthritis, and musculoskeletal problems. Presence, cancer bone metastasis, osteoporosis, lung or major abdominal organ surgery, ostomy, cardiopulmonary disease, lymphedema, extreme fatigue, presence of severe anemia, surgery Whether it is immediately after, the exacerbation of somatic symptoms, and the presence of symptoms of an ongoing infection.

Referring to FIG. 2B, feedback information in which confirmation lists regarding each check box 210 are grouped into a plurality of groups is illustrated. The confirmation lists may be grouped into a plurality of groups. For example, the plurality of groups may be classified into a low risk group 220, a medium risk group 230, a high risk group 240, and a group 250 to avoid exercise for exercise prescription. For example, when the user is in the early stages of cancer, when a high intensity physical activity is performed before cancer diagnosis, and when there is no accompanying disease, the user may be classified into the low risk group 220 for exercise prescription. In addition, when the user has peripheral neuritis, arthritis, musculoskeletal problems, cancer bone metastasis, and osteoporosis are present, the user corresponding to the medium risk group ( 230). In addition, when the user undergoes surgery in the lungs or major abdominal organs, the presence of an ostomy, the presence of cardiopulmonary disease, the presence of lymphedema, and the presence of extreme fatigue, the user corresponding to each Can be classified into a high risk group 240. In addition, users with severe anemia, immediately after surgery, when physical symptoms worsen, and when there are ongoing infection symptoms may be classified into a group 250 that should avoid exercise with respect to exercise prescription. have.

The exercise prescription device may sense user interaction for each group. For example, when the user interacts with the three checkboxes regarding the group to avoid exercise, the exercise prescription device may determine the user as the group to avoid the exercise. In another embodiment, when the user interacts with each of the checkboxes for the low risk group, the medium risk group, and the high risk group, the exercise prescription device may determine the user as a high risk group.

In one embodiment, the exercise prescription device may determine a risk group having the highest risk among the groups to which the user interacts with the checkbox as a group including the user.

In another embodiment, the exercise prescription device may determine a group in which the user is included by applying the number of checkboxes that the user interacts with and the weighting corresponding to each group to which the checkbox belongs.

The exercise prescription device according to the present exemplary embodiment may obtain, as feedback information, a response regarding a user's health state and exercise at predetermined intervals. In addition, the exercise prescription device may expect an effect of generating more detailed exercise prescription data for the user based on the obtained feedback information.

Determination of the First Exercise Prescription Data

3 is a flowchart illustrating a process of determining, by an exercise prescription device, first exercise prescription data according to an embodiment. Referring to FIG. 3, the exercise prescription device selects the survival probability data corresponding to the type of cancer of the user 310 and the first exercise prescription according to a physical ability indicating a survival probability above a threshold in the selected survival probability data. Determining the data may include 320. In operation 310, the exercise prescription device may select survival probability data mapped to disease information from a database. The database may store survival probability data mapped to each of a plurality of disease information. In exemplary embodiments, the database may store, as the survival probability data, a survival probability of the user over time corresponding to a metabolic equivalent task (MET) range per week. More specifically, the database may store the survival probability of the user over time corresponding to each cancer type as the survival probability data.

In another embodiment, the database may store the survival probability of the user over time corresponding to the grip strength range of the user as the survival probability data. In another embodiment, the database may store the survival probability of the user over time corresponding to the 6 minute walking distance of the user as the survival probability data.

4 is an exemplary diagram illustrating survival probability data according to an embodiment. Referring to FIG. 4, survival probability data is shown indicating mortality of cancer patients over time. The X axis of the survival probability data represents time (year, year), the Y axis represents the mortality rate of cancer patients. In the survival probability data, the first graph 410 represents the mortality rate of cancer patients having an exercise amount of 3 MET or less per week. In addition, the second graph 420 represents the mortality rate of cancer patients with an amount of exercise greater than 3 MET per week or less than 8.9 MET per week. In addition, the third graph 430 shows the mortality rate of cancer patients with an amount of exercise of 9 MET or more per week. The exercise prescription device may compare the survival probability of the predetermined threshold 440 with the plurality of graphs 410, 420, and 430. According to the comparison result, the exercise prescription device may generate first exercise prescription data for the user. For example, when the predetermined threshold 440 is set to a mortality rate of 10% or less for 10 years, the exercise prescription device may determine first exercise prescription data to have an exercise amount of 9 MET or more per week. More specifically, the first exercise prescription data may include an exercise type, an exercise intensity, an exercise frequency, and an exercise time.

The exercise prescription device may determine the first exercise prescription data by selecting a plurality of exercise types corresponding to the calculated target exercise amount. More specifically, the exercise prescription device may select a plurality of exercise types corresponding to the disease information of the user. The exercise type corresponding to the disease information of the user may represent a pre-mapped exercise based on a symptom or a risk according to the disease. For example, the database in the exercise prescription device may store the exercise amount MET corresponding to each exercise type and exercise intensity as shown in Table 1 below.

Kind of exercise Exercise intensity Momentum (MET / week) walking Ha 2.5 walking medium 3.5 walking Prize 4.5 wheeling Ha 4.0 wheeling medium 6.0 Climb stairs 6.0 aerobic 6.0 golf 4.5 Badminton 4.5 Dance (doubles) 4.5 Running Ha 8.0 Running medium 9.0 Jump Rope 7.0 Mountain climbing 7.0 swimming 8.0 Cleaning 3.3 Car Wash 4.5 Playing with a dog 4.0 Playing with children 4.0 Reading 1.0 Playing the instrument 2.0 Computer 3.0

In exemplary embodiments, the exercise prescription device may generate first exercise prescription data using flexible exercise as an exercise type to a breast cancer patient having upper extremity dysfunction. For example, the flexible movement may include forward flexion, abduction and adduction, external rotation, elbow rotation, internal rotation, shaking hands behind the back, pushing against the back, hand pushing behind the back, twisting the upper body, and the like. It may include at least one of the T-shape exercise. More specifically, the exercise prescription device may set the thickness of the band required to perform the flexibility exercise using the feedback information on the exercise of the breast cancer patient. The thickness of the band can be identified according to the color of each band. The first exercise prescription data may include a thickness of a band for performing a flexible exercise.

The exercise prescription device may generate first exercise prescription data using a core exercise as a type of exercise in a gynecological cancer patient with neuralgia, initial symptoms of lymphedema, muscle pain, and pubic region pain. In addition, the exercise prescription device may generate first exercise prescription data using a core exercise as an exercise type even for cancer patients suffering from either colorectal cancer or prostate cancer. The core exercise may include any one of a transverse abdominal muscle exercise, abdominal muscle exercise, a radiator muscle exercise, abdominal muscle exercise, limbs staggered, cross-legs stretch.

In another embodiment, the exercise prescription device may generate first exercise prescription data using at least one of a bike ride and a walk in water as the exercise type for the gynecological cancer patient. The exercise prescription device according to the present embodiment may generate exercise prescription data having an appropriate exercise type and exercise intensity in consideration of surgical site pain, sequelae, and physical dysfunction according to various cancer types. Accordingly, users may obtain exercise prescription data including exercise prescriptions that are better suited to them rather than batch exercise prescriptions.

In another embodiment, the exercise prescription device may generate first exercise prescription data using chest expansion exercise as a type of exercise in a lung cancer patient in need of rehabilitation and dysfunction rehabilitation. In addition, the exercise prescription device may generate first exercise prescription data for lung cancer patients using tree making exercise, T-shape exercise, cough and breathing exercise, and rib cage stretching as exercise types.

The above-described exercise prescription data for each cancer type that the user is suffering from are merely exemplary descriptions for better understanding and should not be construed as limiting or limiting the scope of other embodiments.

Generation of Second Exercise Prescription Data

5 is a flowchart illustrating a process of generating, by the exercise prescription device of FIG. 3, second exercise prescription data using feedback information. Referring to FIG. 5, a process in which the exercise prescription device 510 generates new second exercise prescription data 530 by comparing the previously generated first exercise prescription data 520 with feedback information is illustrated. The exercise prescription device 510 may receive feedback information about an exercise performed by a user. In addition, the exercise prescription device 510 may classify a risk group for the user's health condition and symptoms by using the feedback information. For example, the exercise prescription device 510 may classify the user into one of a low risk group, a medium risk group, a high risk group, and a group to avoid exercise using the feedback information.

The exercise prescription device 510 may store the parasitic first exercise prescription data 520. The first exercise prescription data 520 may include the number of exercise times of 1 day or more and 3 days or less, light to moderate exercise intensity, 20 minutes of exercise time per session, and light walking and strength exercises. have. For example, there may be a case where a user is classified into a low risk group by the exercise prescription device 510. In this case, the exercise prescription device 510 may correct the first exercise prescription data 520 with the second exercise prescription data 530 according to the feedback information. The second exercise prescription data 530 shows the frequency of exercise up to 5 days a week, the intensity of light to moderate exercise, the exercise time of 20 minutes to 60 minutes per session and light walking, strength exercise and aerobic exercise. It can be included as. In the present embodiment, an embodiment in which the second exercise prescription data is generated in which the number and intensity of the user's feedback information is classified as a low risk group is increased than the first exercise prescription data, but this is to limit or limit the scope of other embodiments. It should not be interpreted as. For example, if the user is found to have lymphedema or severe fatigue symptoms and the feedback information is classified as a high risk group, the second exercise prescription data may be generated in which the number, intensity, and time are reduced from the first exercise prescription data. will be.

6 is an exemplary view illustrating a process of outputting a notification message to a user by the exercise prescription device according to another embodiment. Referring to FIG. 6, the exercise prescription device 600 outputs a vibration or notification message to the user 610. The exercise prescription device 600 may receive cumulative exercise result data 620 about the user 610 from a predetermined device. The predetermined device may represent a wearable device worn by the user 610 to collect data about walking of the user 610. In exemplary embodiments, the predetermined device may represent a smart insole paired with the exercise prescription device 600. The cumulative exercise result data 620 may be received in the form of daily exercise amount MET.

The exercise prescription device 600 may compare the sum result of the pre-stored target exercise amount 630 with the daily exercise amount. For example, the target exercise amount 630 may be set to 9 MET per week. In this case, the exercise prescription device 600 may determine that the sum of the exercise amounts does not reach the target exercise amount 630 on the seventh day of the week. The exercise prescription device 600 may output a vibration or notification message to the user 610 together with the difference value of the accumulated exercise amount with respect to the target exercise amount 630. The user 610 may determine that the current amount of exercise is insufficient from the vibration or notification message of the exercise prescription device 600, and may further perform the exercise prescribed to the user.

Figure 7 is a block diagram of an exercise prescription device according to another embodiment. Referring to FIG. 7, the computing device 700 may include a communication unit 710, an input unit 720, a determination unit 730, a correction unit 740, and an output unit 750. Computing device 700 may represent an exercise prescription device that provides a fit exercise prescription to a user. The communicator 710 may receive exercise result data for the user from a predetermined device. In exemplary embodiments, the predefined device may represent a smart insole device paired with computing device 700. The exercise result data may represent data including a user's sitting time, walking speed, walking distance, and walking time.

The input unit 720 may obtain feedback information on the received exercise result data. In exemplary embodiments, the feedback information may include data indicative of the user's health condition, symptoms, and physical changes to the exercise performed. As an example, the input unit 720 may call a check list for identifying symptoms for each of the lymphedema, peripheral neuropathy, immune suppression, fatigue symptoms, and musculoskeletal disorders based on the received exercise result data.

The determination unit 730 may determine the first exercise prescription data including the exercise type, the exercise intensity, the number of exercise, and the exercise time by referring to the disease information and the state information from the database of the user. The database may map and store the recommended exercise types for a particular disease. In addition, the database may store and store the appropriate exercise intensity, the number of times and time with the disease information based on the survival probability data for the particular disease. The status information may include health status and symptom information of the user. The database may sequentially store state information associated with a user in time series order.

In addition, the determination unit 730 may select survival probability data matching the disease information from the database. The determination unit 730 may determine the first exercise prescription data according to a physical ability indicating a survival probability above a threshold value in the selected survival probability data. For example, in the case of lung cancer patients, if you have a walking distance of more than 300m 6 minutes to expect a survival probability of more than the threshold, the determination unit 630 may determine the first exercise prescription data corresponding to the 6-minute walking distance.

More specifically, the determination unit 730 may refer to the type of cancer of the user stored in the database, and select survival probability data corresponding to the type of cancer. The survival probability data may be various types of data representing survival probability according to physical ability and cancer type, such as survival probability according to grip force, survival probability based on exercise amount per week (MET), and survival probability based on 6 minutes walking distance. .

The corrector 740 may correct the first exercise prescription data according to the feedback information to generate second exercise prescription data of the user. The corrector 740 may classify the user into one of a plurality of groups based on the feedback information of the user. The corrector 740 may correct the first exercise prescription data as the second exercise prescription data according to the classified group of the user. The corrector 740 may generate the second exercise prescription data by increasing or decreasing the number of times of exercise, exercise intensity, and exercise time included in the first exercise prescription data by a predetermined ratio according to the classified group of the user. By way of example, the plurality of groups may include a low risk group, a medium risk group, a high risk group, and a group to avoid exercise.

The output unit 750 may compare the exercise result data with the second exercise prescription data, and output a notification message to the user according to the comparison result. The output unit 750 may output a plurality of graphic objects corresponding to the check list. In this case, the input unit 720 may obtain the feedback information according to a user interaction with respect to the graphic object.

In another embodiment, the communication unit 710 may update the exercise result data of the user according to a predetermined time period. In exemplary embodiments, the communication unit 710 may update the daily exercise amount as exercise result data. The output unit 750 may output a notification message to the user based on the target exercise amount included in the second exercise prescription data and the exercise amount difference value of the updated exercise result data.

In another embodiment, the communication unit 710 may receive the exercise result data including the seating time of the user. The output unit 750 may output a notification message to the user based on the difference between the seating time limit included in the second exercise prescription data and the seating time of the user.

In another embodiment, the communication unit 710 may receive the cumulative exercise result data of the user summed for a predetermined period of time. The corrector 740 may correct the second exercise prescription data using the cumulative exercise result data. For example, when the actual exercise amount is less than 1.5 MET compared to the target exercise amount of the exercise prescription data last week, the correction unit 740 may correct the second exercise prescription data to increase the target exercise amount of the exercise prescription data by 1.5 MET. Can be. The corrector 740 may correct the second exercise prescription data at a future time based on the difference between the target exercise amount and the actual exercise amount.

Although not shown in FIG. 7, the description of the computing device 700 including the exercise prescription device may also apply to the exercise prescription method. In addition, the present invention may be applied to a recording medium which executes such exercise prescription method. In exemplary embodiments, the exercise prescription method may further include obtaining feedback information on the received exercise result data, retrieving disease information and state information from a database regarding the user, and type of exercise according to the retrieved disease information and state information. Determining first exercise prescription data including exercise intensity, exercise count, and exercise time; and correcting the first exercise prescription data according to the feedback information to generate second exercise prescription data of the user. It may include.

The embodiments described above may be implemented as hardware components, software components, and / or combinations of hardware components and software components. For example, the devices, methods, and components described in the embodiments may include, for example, processors, controllers, arithmetic logic units (ALUs), digital signal processors, microcomputers, field programmable gates (FPGAs). It may be implemented using one or more general purpose or special purpose computers, such as an array, a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to the execution of the software. For convenience of explanation, one processing device may be described as being used, but one of ordinary skill in the art will appreciate that the processing device includes a plurality of processing elements and / or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as parallel processors.

The software may include a computer program, code, instructions, or a combination of one or more of the above, and may configure the processing device to operate as desired, or process independently or collectively. You can command the device. Software and / or data may be any type of machine, component, physical device, virtual equipment, computer storage medium or device in order to be interpreted by or to provide instructions or data to the processing device. Or may be permanently or temporarily embodied in a signal wave to be transmitted. The software may be distributed over networked computer systems so that they may be stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to the embodiment may be embodied in the form of program instructions that can be executed by various computer means and recorded on a computer readable medium. Computer-readable media may include, alone or in combination with the program instructions, data files, data structures, and the like. The program instructions recorded on the computer readable medium may be those specially designed and constructed for the purposes of the embodiments, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks such as floppy disks. Magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the embodiments have been described with reference to the accompanying drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques may be performed in a different order than the described method, and / or components of the described systems, structures, devices, circuits, etc. may be combined or combined in a different form than the described method, or other components. Or, even if replaced or substituted by equivalents, an appropriate result can be achieved.

Claims (16)

A communication unit configured to receive exercise result data for a user from a designated device;
An input unit to obtain feedback information on the received exercise result data;
A determination unit for determining first exercise prescription data including exercise type, exercise intensity, exercise number, and exercise time by referring to disease information and state information from the database of the user; And
A correction unit configured to correct the first exercise prescription data according to the feedback information to generate second exercise prescription data of the user
Including,
The determination unit selects survival probability data matching the disease information from the database, and determines the first exercise prescription data according to a physical ability indicating a survival probability above a threshold in the selected survival probability data,
Computing device.

delete The method of claim 1,
The determination unit selects survival probability data corresponding to the cancer type by referring to the cancer type corresponding to the user from the database, and the survival probability graph corresponds to a metabolic equivalent task (MET) range. Computing device comprising the probability of survival over time.
The method of claim 1,
And the input unit calls a check list for confirming symptoms for each of lymphedema, peripheral neuropathy, immunodeficiency, fatigue symptoms, and musculoskeletal disorders based on the received exercise result data.
The method of claim 4, wherein
An output unit for comparing the exercise result data with the second exercise prescription data and outputting a notification message to the user according to the comparison result
Computing device further comprising.
The method of claim 5,
And the output unit outputs a graphic object corresponding to the check list, and the input unit obtains the feedback information according to a user interaction with respect to the graphic object.
The method of claim 5,
The communicator updates the workout result data of the user according to a predetermined time period, and the output unit is configured to output a notification message to the user based on an exercise amount difference value of the second workout prescription data and the updated workout result data. Device.
The method of claim 5,
The communication unit receives the exercise result data including a seating time of the user, and the output unit notifies the user based on a difference between a seating time limit included in the second exercise prescription data and a seating time of the user. A computing device that outputs a message.
The method of claim 1,
And the communication unit receives cumulative exercise result data of the user summed up for a predetermined period, and the correction unit corrects the second exercise prescription data using the cumulative exercise result data.
The method of claim 1,
The communication unit receives the exercise result data from a smart insole measuring acceleration data, velocity data, direction data, and distance data about the user as the designated device, and the smart insole is connected to the computing device through a designated communication interface. Computing device.
Obtaining feedback information on the received exercise result data;
Querying for disease information and status information from a database about the user;
Determining first exercise prescription data including exercise type, exercise intensity, exercise number, and exercise time according to the inquired disease information and state information; And
Correcting the first exercise prescription data according to the feedback information to generate second exercise prescription data of the user
Including,
Determining the first exercise prescription data,
Selecting survival probability data matching the disease information from the database, and determining the first exercise prescription data according to a physical ability indicating survival probability above a threshold in the selected survival probability data,
Exercise prescription method.
The method of claim 11,
Inquiring of the disease information and the state information,
Querying risk group information about cancer type information and symptoms corresponding to the user from the database;
Including,
Determining the first exercise prescription data,
Selecting survival probability data according to cancer type information corresponding to the user, and determining the first exercise prescription data according to a physical ability indicating a survival probability above a threshold in the selected survival probability data.
Exercise prescription method comprising a.
The method of claim 12,
Acquiring the feedback information,
Calling a checklist for identifying symptoms for each of lymphedema, peripheral neuropathy, immunodeficiency, fatigue symptoms and musculoskeletal disorders based on the received exercise result data; And
Acquiring the feedback information according to a user interaction associated with the check list
Exercise prescription method comprising a.
The method of claim 13,
Comparing the exercise result data with the second exercise prescription data and outputting a notification message to the user according to the comparison result
Exercise prescription method further comprising.
The method of claim 13,
Updating risk group information about symptoms in the database using the obtained feedback information;
Exercise prescription method further comprising.
A computer-readable recording medium containing a program for executing an exercise prescription method, the program comprising:
An instruction set for obtaining feedback information on the received exercise result data;
An instruction set for querying disease information and status information from a database about a user;
An instruction set for determining first exercise prescription data including exercise type, exercise intensity, exercise number, and exercise time according to the inquired disease information and state information; And
An instruction set for correcting the first exercise prescription data according to the feedback information to generate second exercise prescription data of the user;
Including,
Instruction set for determining the first exercise prescription data,
A set of instructions for selecting survival probability data matching the disease information from the database, and determining the first exercise prescription data according to a physical ability indicating survival probability above a threshold in the selected survival probability data;
Computer-readable recording medium.

Images